Reusable components in decision tree induction algorithms
We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE,...
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| Published in: | Computational statistics Vol. 27; no. 1; pp. 127 - 148 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer-Verlag
01.03.2012
Springer Nature B.V |
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| ISSN: | 0943-4062, 1613-9658 |
| Online Access: | Get full text |
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| Abstract | We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their partial improvements that can be used as solutions for sub-problems in the generic decision tree framework. The identified components can now be used outside the algorithm they originate from. Combining reusable components allows the replication of original algorithms, their modification but also the creation of new decision tree induction algorithms. Every original algorithm can outperform other algorithms under specific conditions but can also perform poorly when these conditions change. Reusable components allow exchanging of solutions from various algorithms and fast design of new algorithms. We offer a generic framework for component-based algorithms design that enhances understanding, testing and usability of decision tree algorithm parts. |
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| AbstractList | We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their partial improvements that can be used as solutions for sub-problems in the generic decision tree framework. The identified components can now be used outside the algorithm they originate from. Combining reusable components allows the replication of original algorithms, their modification but also the creation of new decision tree induction algorithms. Every original algorithm can outperform other algorithms under specific conditions but can also perform poorly when these conditions change. Reusable components allow exchanging of solutions from various algorithms and fast design of new algorithms. We offer a generic framework for component-based algorithms design that enhances understanding, testing and usability of decision tree algorithm parts. We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their partial improvements that can be used as solutions for sub-problems in the generic decision tree framework. The identified components can now be used outside the algorithm they originate from. Combining reusable components allows the replication of original algorithms, their modification but also the creation of new decision tree induction algorithms. Every original algorithm can outperform other algorithms under specific conditions but can also perform poorly when these conditions change. Reusable components allow exchanging of solutions from various algorithms and fast design of new algorithms. We offer a generic framework for component-based algorithms design that enhances understanding, testing and usability of decision tree algorithm parts.[PUBLICATION ABSTRACT] |
| Author | Vukicevic, Milan Becejski-Vujaklija, Dragana Obradovic, Zoran Delibasic, Boris Jovanovic, Milos Suknovic, Milija |
| Author_xml | – sequence: 1 givenname: Milija surname: Suknovic fullname: Suknovic, Milija organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 2 givenname: Boris surname: Delibasic fullname: Delibasic, Boris email: boris.delibasic@fon.bg.ac.rs organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 3 givenname: Milos surname: Jovanovic fullname: Jovanovic, Milos organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 4 givenname: Milan surname: Vukicevic fullname: Vukicevic, Milan organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 5 givenname: Dragana surname: Becejski-Vujaklija fullname: Becejski-Vujaklija, Dragana organization: Faculty of Organizational Sciences, University of Belgrade – sequence: 6 givenname: Zoran surname: Obradovic fullname: Obradovic, Zoran organization: Information Science and Technology Center, Temple University |
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| Cites_doi | 10.1109/52.976942 10.1023/A:1007608224229 10.1109/21.97458 10.2307/2346830 10.1016/S0020-7373(87)80053-6 10.1145/382296.382702 10.1007/s10462-009-9133-6 10.1023/A:1007413323501 10.1198/106186006X133933 10.1007/BF00153761 10.1023/A:1008920224518 10.1023/A:1022604100933 10.2307/2986296 10.1198/106186008X319331 10.1007/978-3-540-78246-9_39 10.2307/2289295 10.3233/IDA-2011-0489 10.1023/A:1009744630224 10.1145/1150402.1150531 10.1023/A:1022694001379 10.1007/978-1-4612-2404-4_35 |
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| Snippet | We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several... |
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| SubjectTerms | Algorithms Carts Decision trees Economic Theory/Quantitative Economics/Mathematical Methods Exchanging Machine learning Mathematical analysis Mathematical models Mathematics and Statistics Open source software Original Paper Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Replication Reusable components Statistics Studies |
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| Title | Reusable components in decision tree induction algorithms |
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